app.py

from langchain_community.vectorstores import Qdrant
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
import os
from dotenv import load_dotenv
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from qdrant_client import QdrantClient, models
from langchain_qdrant import Qdrant
import gradio as gr

# Load environment variables
load_dotenv()

os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API")

# HuggingFace Embeddings
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")

# Qdrant Client Setup
client = QdrantClient(
    url=os.getenv("QDRANT_URL"),
    api_key=os.getenv("QDRANT_API_KEY"),
    prefer_grpc=True
)

collection_name = "mawared"

# Try to create collection, handle if it already exists
try:
    client.create_collection(
        collection_name=collection_name,
        vectors_config=models.VectorParams(
            size=768,  # GTE-large embedding size
            distance=models.Distance.COSINE
        ),
    )
    print(f"Created new collection: {collection_name}")
except Exception as e:
    if "already exists" in str(e):
        print(f"Collection {collection_name} already exists, continuing...")
    else:
        raise e

# Create Qdrant vector store
db = Qdrant(
    client=client,
    collection_name=collection_name,
    embeddings=embeddings,
)

# Create retriever
retriever = db.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 5}
)

# LLM setup
llm = ChatGroq(
    model="llama-3.3-70b-versatile",
    temperature=0.1,
    max_tokens=None,
    timeout=None,
    max_retries=2,
)

# Create prompt template
template = """
You are an expert assistant specializing in the LONG COT RAG. Your task is to answer the user's question strictly based on the provided context...
Context:
{context}

Question:
{question}

Answer:
"""

prompt = ChatPromptTemplate.from_template(template)

# Create the RAG chain
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Define the Gradio function
def ask_question_gradio(question):
    result = ""
    for chunk in rag_chain.stream(question):
        result += chunk
    return result

# Create the Gradio interface
interface = gr.Interface(
    fn=ask_question_gradio,
    inputs="text",
    outputs="text",
    title="Mawared Expert Assistant",
    description="Ask questions about the Mawared HR System or any related topic using Chain-of-Thought (CoT) and RAG principles.",
    theme="compact",
)

# Launch Gradio app
if __name__ == "__main__":
    interface.launch()